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Business Analytics
Jun 2026 Examination
Q1. A national retail
chain, FreshStyles, is facing declining sales and customer complaints about
product availability. The management suspects that the underlying issue stems
from inconsistencies in their sales and inventory data collected from multiple
branches. Their current datasets contain missing values, duplicates, and
inconsistent formatting in date and product codes. Despite using Excel for
analysis, the results remain inconclusive and are met with skepticism by
stakeholders. The company’s analytics team has been tasked with resolving these
data issues to enable trustworthy business insights and inform better inventory
and sales strategies.As the lead data analyst for FreshStyles, apply
appropriate data cleansing techniques (including missing value treatment,
duplicate removal, and format standardization) to this real- world dataset.
Describe the sequential steps you would take and explain how your approach
ensures data reliability and supports more effective business decision- making?
(10 Marks)
Ans 1.
Introduction
FreshStyles
which is a nationwide retail chain, currently faces falling sales and
increasing complaints about product availability. The root of the problem
appears to have to do with poor data quality, resulting from multiple branches,
where data sets contain missing values as well as duplicate data and different
formats for product codes and dates. These inconsistencies render the analysis
inconclusive, even using applications such as Excel which has led to low the
confidence of stakeholders. As the lead data analyst your primary goal is to
implement a structured data cleansing method to turn this data
Q2(A). A
manufacturing business has recently implemented a probability distribution
analysis to better understand and reduce process defects. The operation team is
considering whether to fit the data to a Poisson (discrete, PMF-based) or an
Exponential (continuous, PDF-based) distribution. Corporate leadership is
concerned about the accuracy and effectiveness of using each approach to drive
quality improvement initiatives and continuous adaptation.Critically evaluate
the merits and drawbacks of modeling defect data using Poisson versus
Exponential distributions. Assess how the choice between the two would impact
quality assurance, predictive accuracy, and the company's adaptability to
dynamic production environments, justifying your position. (5 Marks)
Ans 2a.
Introduction
A
company in the manufacturing industry is employing probability distributions
for analyzing the quality of its products and identify defects. Making the
right choice between Poisson as well as Exponential distributions is essential
because it has a direct impact on how the defects are perceived, forecasted and
managed. The choice should be in line with what the nature of data is and
operating realities.
Concept and Application
Practically,
choosing the proper distribution isn't only an option for statistical reasons,
but rather a decision that is strategic
Q2(B). A consumer
goods company deploys a simple linear regression model to predict monthly sales
from advertising spend, yielding an R-squared value of 0.82. However, regional
marketing managers note that in some months, major events (such as festivals
and supply chain disruptions) may cause large, unpredictable deviations in
sales that the regression model does not explain. The executive team must
decide how much to trust the model outputs for future campaign planning, and
whether to introduce more explanatory variables or develop alternative
analytics approaches.Critique the company’s reliance on the current regression
model for campaign planning in light of the marketing managers' observations.
How should the executive team weigh the strong R-squared value against external
factors, and what improvements or complementary analyses would you recommend to
enhance decision-making robustness? (5 Marks)
Ans 2b.
Introduction
The
regression model of the company provides a robust R-squared figure of 0.82
which indicates a positive connection between the amount of advertising spent
and sales. But in reality, factors like festivals or disruptions produce
variations that the model cannot explain. This is why it's not advisable to
rely entirely on models for deliberation.
Concept and Application
In
terms of analytics and data analysis, a very high R-squared does not ensure
total accuracy. Modelling must be assessed non only on a statistical basis, but
on the basis of their real-world application
Dear
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Do
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call us at : 08263069601
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